All language subtitles for KU PMGT 823 Session 4 (Part B)- Quantitative
Afrikaans
Akan
Albanian
Amharic
Arabic
Armenian
Azerbaijani
Basque
Belarusian
Bemba
Bengali
Bihari
Bosnian
Breton
Bulgarian
Cambodian
Catalan
Cebuano
Cherokee
Chichewa
Chinese (Simplified)
Chinese (Traditional)
Corsican
Croatian
Czech
Danish
Dutch
English
Esperanto
Estonian
Ewe
Faroese
Filipino
Finnish
French
Frisian
Ga
Galician
Georgian
German
Greek
Guarani
Gujarati
Haitian Creole
Hausa
Hawaiian
Hebrew
Hindi
Hmong
Hungarian
Icelandic
Igbo
Indonesian
Interlingua
Irish
Italian
Japanese
Javanese
Kannada
Kazakh
Kinyarwanda
Kirundi
Kongo
Korean
Krio (Sierra Leone)
Kurdish
Kurdish (SoranĂ®)
Kyrgyz
Laothian
Latin
Latvian
Lingala
Lithuanian
Lozi
Luganda
Luo
Luxembourgish
Macedonian
Malagasy
Malay
Malayalam
Maltese
Maori
Marathi
Mauritian Creole
Moldavian
Mongolian
Myanmar (Burmese)
Montenegrin
Nepali
Nigerian Pidgin
Northern Sotho
Norwegian
Norwegian (Nynorsk)
Occitan
Oriya
Oromo
Pashto
Persian
Polish
Portuguese (Brazil)
Portuguese (Portugal)
Punjabi
Quechua
Romanian
Romansh
Runyakitara
Russian
Samoan
Scots Gaelic
Serbian
Serbo-Croatian
Sesotho
Setswana
Seychellois Creole
Shona
Sindhi
Sinhalese
Slovak
Slovenian
Somali
Spanish
Spanish (Latin American)
Sundanese
Swahili
Swedish
Tajik
Tamil
Tatar
Telugu
Thai
Tigrinya
Tonga
Tshiluba
Tumbuka
Turkish
Turkmen
Twi
Uighur
Ukrainian
Urdu
Uzbek
Vietnamese
Welsh
Wolof
Xhosa
Yiddish
Yoruba
Zulu
Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated:
1
00:00:01,230 --> 00:00:06,650
Hello, everyone, and welcome to Part 2
of Session 4 in PMGT 823, Project Risk
2
00:00:06,650 --> 00:00:11,950
Management. In Part A, we focused on
qualitative risk analysis, how to
3
00:00:11,950 --> 00:00:15,890
and prioritize risks based on expert
judgment and experience.
4
00:00:16,329 --> 00:00:20,790
Now, in Part B, we will take things a
step further with quantitative risk
5
00:00:20,790 --> 00:00:25,930
analysis. We will explore how to use
numerical tools and models to estimate
6
00:00:25,930 --> 00:00:29,370
overall impact of risks on project
outcomes. Let's get started.
7
00:00:32,270 --> 00:00:36,050
In this part of the module, we will
focus on quantitative risk analysis.
8
00:00:36,730 --> 00:00:42,190
Unlike qualitative analysis, which is
based on judgment and prioritization,
9
00:00:42,190 --> 00:00:46,030
approach uses numerical methods to
measure overall risk exposure.
10
00:00:46,450 --> 00:00:51,470
We will look at techniques like expected
monetary value, decision tree analysis,
11
00:00:51,650 --> 00:00:53,330
and Monte Carlo simulation.
12
00:00:53,850 --> 00:00:56,750
So if you are ready to dive into some
numbers, let's go.
13
00:00:59,880 --> 00:01:04,220
Let's return to the full risk management
process to see where quantitative risk
14
00:01:04,220 --> 00:01:05,280
analysis fits in.
15
00:01:05,900 --> 00:01:10,840
After planning for risk management,
identifying risks, and performing a
16
00:01:10,840 --> 00:01:15,140
qualitative analysis, finally we arrive
at step four that is highlighted here.
17
00:01:15,840 --> 00:01:20,800
This step uses numerical methods to
estimate the combined effects of the
18
00:01:20,800 --> 00:01:22,620
on overall project objectives.
19
00:01:23,000 --> 00:01:29,280
It is especially helpful when decisions
involve significant costs, timelines, or
20
00:01:29,280 --> 00:01:36,040
resource trade -offs similar to the
previous part we have two
21
00:01:36,040 --> 00:01:40,900
important questions here what exactly is
quantitative data and why is it
22
00:01:40,900 --> 00:01:46,340
important in risk analysis well
quantitative data is information you can
23
00:01:46,340 --> 00:01:52,560
count or verify unlike qualitative data
like i drink coffee every day
24
00:01:52,560 --> 00:01:56,600
quantitative data tells us exactly how
much or how often
25
00:01:57,360 --> 00:02:03,500
For example, I drink four cups of coffee
per day or consume 80 grams of coffee.
26
00:02:03,760 --> 00:02:06,940
Both of them are measurable numerical
statements.
27
00:02:07,820 --> 00:02:12,780
In the context of project risk, we use
this kind of data to analyze the
28
00:02:12,780 --> 00:02:17,960
impact of all risks on project
objectives, such as total cost,
29
00:02:17,960 --> 00:02:19,900
variance, or potential delays.
30
00:02:20,520 --> 00:02:24,880
So one more time, what is the real
benefit of using quantitative risk
31
00:02:25,720 --> 00:02:30,960
It gives us a clearer picture of our
overall risk exposure and it helps us
32
00:02:30,960 --> 00:02:33,720
better informed decisions about the risk
responses.
33
00:02:35,820 --> 00:02:38,840
Quantitative risk analysis has two main
benefits.
34
00:02:39,320 --> 00:02:44,440
First, it is the only reliable method
for assessing the overall level of
35
00:02:44,440 --> 00:02:50,200
risk because it evaluates the combined
impact of all risks and uncertainties.
36
00:02:50,830 --> 00:02:55,870
Second, it gives us quantitative data
that supports smarter decision -making.
37
00:02:56,290 --> 00:03:02,370
With this data, we can identify which
risks deserve more attention and how we
38
00:03:02,370 --> 00:03:06,130
should respond to them, whether it is
setting aside more budget, adjusting
39
00:03:06,130 --> 00:03:08,950
schedules, or preparing contingency
plans.
40
00:03:09,690 --> 00:03:12,630
But to do this properly, we need a few
things.
41
00:03:13,030 --> 00:03:15,730
First, we need high -quality risk data.
42
00:03:17,760 --> 00:03:22,420
we also need a clear baseline for scope,
schedule, and cost.
43
00:03:22,720 --> 00:03:28,560
And of course, sometimes we even need
specialized software or expert support
44
00:03:28,560 --> 00:03:30,100
build and interpret models.
45
00:03:32,880 --> 00:03:36,680
So when should you actually perform
quantitative risk analysis?
46
00:03:37,300 --> 00:03:42,460
It is best suited for large or complex
projects, projects with contractual
47
00:03:42,460 --> 00:03:46,240
requirements, or where stakeholders
explicitly request it.
48
00:03:46,890 --> 00:03:51,150
You should also consider it when the
project is strategically important for
49
00:03:51,150 --> 00:03:55,810
organization or if it is sensitive to
delays and overruns.
50
00:03:56,070 --> 00:04:00,490
But even in these cases, you need to
check a few things before jumping in.
51
00:04:01,570 --> 00:04:06,590
So say yes to quantitative analysis if
you have the necessary tools and data.
52
00:04:06,970 --> 00:04:10,550
You're confident that most key risks
have been identified.
53
00:04:11,450 --> 00:04:16,070
You have budget and time for the
analysis, and there is low tolerance for
54
00:04:16,070 --> 00:04:17,370
or schedule deviations.
55
00:04:17,890 --> 00:04:23,470
On the other hand, if your data isn't
accurate or subjective inputs work just
56
00:04:23,470 --> 00:04:28,090
well for you, then a qualitative
approach might be more efficient and
57
00:04:28,090 --> 00:04:29,090
-effective.
58
00:04:31,170 --> 00:04:34,870
Here is the big picture of the
quantitative risk analysis process.
59
00:04:35,410 --> 00:04:38,430
On the left, we start with a solid set
of inputs.
60
00:04:38,780 --> 00:04:43,760
including the project management plan,
risk register, and reliable cost and
61
00:04:43,760 --> 00:04:44,760
schedule estimates.
62
00:04:44,840 --> 00:04:48,480
Without these inputs, the analysis
simply won't work.
63
00:04:48,800 --> 00:04:53,140
In the center, we see a range of tools
and techniques used to perform the
64
00:04:53,140 --> 00:04:58,440
analysis. These include expert judgment
and interviews, visual models like
65
00:04:58,440 --> 00:05:03,100
influence diagrams, and most
importantly, simulation tools,
66
00:05:03,100 --> 00:05:05,280
analysis, and decision tree analysis.
67
00:05:06,120 --> 00:05:10,040
We will cover each of these in more
detail in upcoming slides.
68
00:05:12,100 --> 00:05:16,400
Now let's look at the main tools and
techniques used in quantitative risk
69
00:05:16,400 --> 00:05:21,620
analysis. We usually begin with the
expert judgment, which plays a crucial
70
00:05:21,740 --> 00:05:27,000
especially when translating qualitative
insights into numeric estimates or when
71
00:05:27,000 --> 00:05:28,540
interpreting complex results.
72
00:05:29,320 --> 00:05:34,000
Next, we have data gathering, often done
through interviews with SMEs to get
73
00:05:34,000 --> 00:05:37,680
accurate data on risk probabilities,
impact, and dependencies.
74
00:05:38,460 --> 00:05:43,540
Team skills are also vital because they
help ensure the group state focus,
75
00:05:43,880 --> 00:05:48,500
minimize conflict, and support consensus
building during risk workshops.
76
00:05:48,980 --> 00:05:51,760
Then we come to the representation of
uncertainty.
77
00:05:52,780 --> 00:05:57,520
Here, we model uncertainties in things
like cost or time using probability
78
00:05:57,520 --> 00:06:01,740
distributions such as triangular,
normal, or beta distributions.
79
00:06:02,700 --> 00:06:08,700
And finally, we use data analysis
techniques such as simulations to
80
00:06:08,700 --> 00:06:13,880
wide range of possible outcomes,
sensitivity analysis to see which
81
00:06:13,880 --> 00:06:15,380
have the biggest impact,
82
00:06:16,170 --> 00:06:21,630
Decision tree analysis to compare
different options under uncertainty and
83
00:06:21,630 --> 00:06:26,810
influence diagrams to show how variable
and decisions interact visually.
84
00:06:29,610 --> 00:06:33,890
Let's now look at one of the most
powerful tools in quantitative risk
85
00:06:33,890 --> 00:06:35,590
that is Monte Carlo simulation.
86
00:06:36,410 --> 00:06:41,570
This method allows us to evaluate how
uncertainties in cost or time might
87
00:06:41,570 --> 00:06:42,830
the overall project outcomes.
88
00:06:43,450 --> 00:06:45,070
Here is how it works.
89
00:06:45,660 --> 00:06:50,180
We take input values like cost estimates
or activity durations and let the
90
00:06:50,180 --> 00:06:53,220
computer randomly select values from
their defined range.
91
00:06:53,500 --> 00:06:58,440
Then we run the model thousands of times
and what we get as outputs are things
92
00:06:58,440 --> 00:07:03,920
like histograms showing the frequency of
different results such as how often we
93
00:07:03,920 --> 00:07:05,180
finish under budget.
94
00:07:05,420 --> 00:07:10,320
And also S -curves, which gives us the
cumulative probability of meeting a
95
00:07:10,320 --> 00:07:12,340
specific cost or a scheduled target.
96
00:07:12,910 --> 00:07:17,490
For example, if your S -curve shows that
there is only a 60 % chance of
97
00:07:17,490 --> 00:07:21,790
completing project under $10 million,
then it might be a red flag for you.
98
00:07:22,190 --> 00:07:27,070
And also, when we apply simulation to a
scheduled risk, we can do a critical
99
00:07:27,070 --> 00:07:32,310
analysis to find out which activities
are most likely to appear on the
100
00:07:32,310 --> 00:07:33,570
parts of the project.
101
00:08:16,110 --> 00:08:20,610
And here we have decision tree analysis
that is a valuable tool for choosing the
102
00:08:20,610 --> 00:08:23,770
best option when faced multiple project
passes.
103
00:08:24,250 --> 00:08:28,250
Each branch of the tree represents a
decision or a chance event.
104
00:08:28,490 --> 00:08:33,049
For example, in this case, we are
deciding whether to build a new plant or
105
00:08:33,049 --> 00:08:34,610
upgrade the existing one.
106
00:08:35,150 --> 00:08:39,990
The branches continue with possible
future events like a strong or weak
107
00:08:40,590 --> 00:08:45,610
And each endpoint shows the resulting
outcomes that can be either a gain or
108
00:08:45,610 --> 00:08:52,290
loss. Then to evaluate each branch, we
use expected monetary value or EMV,
109
00:08:52,290 --> 00:08:55,910
is the weighted average of all possible
outcomes along that path.
110
00:08:56,490 --> 00:09:01,270
For example, in this case, the decision
to build a new plant leads to higher
111
00:09:01,270 --> 00:09:03,990
EMV. So that becomes the optimal choice.
112
00:09:06,440 --> 00:09:08,460
Now let's walk through this example.
113
00:09:08,780 --> 00:09:13,560
Imagine you are prime contractor of the
project and the contract imposes a $1
114
00:09:13,560 --> 00:09:16,160
,000 penalty per day of late delivery.
115
00:09:16,580 --> 00:09:18,900
Now you have two subcontractor options.
116
00:09:19,820 --> 00:09:22,860
Subcontractor A is the low -cost but
risky option.
117
00:09:23,120 --> 00:09:28,220
They offer a cheaper bid, but there is a
50 % chance of a 90 -day delay, which
118
00:09:28,220 --> 00:09:30,700
could cost you $90 ,000 in penalties.
119
00:09:31,040 --> 00:09:36,070
On the other hand, Subcontractor B is
more expensive but more reliable.
120
00:09:36,350 --> 00:09:41,770
They have only a 10 % chance of being 30
days late, meaning a smaller penalty
121
00:09:41,770 --> 00:09:43,030
that is $30 ,000.
122
00:09:43,430 --> 00:09:45,150
So how do you decide?
123
00:09:45,490 --> 00:09:50,970
You will use Expected Monetary Value or
EMV. For each option, you combine the
124
00:09:50,970 --> 00:09:55,190
original bid with the probability
-weighted penalty to get the expected
125
00:09:55,190 --> 00:09:57,050
cut. Let's see how it works.
126
00:09:58,250 --> 00:10:02,870
Now let's look at how we calculate the
expected monetary value for each option.
127
00:10:03,210 --> 00:10:07,170
First, we start with subcontractor A
that is low but risky choice.
128
00:10:07,990 --> 00:10:13,710
There is a 50 % chance of a $90 ,000
penalty so the total cost could go up to
129
00:10:13,710 --> 00:10:14,710
$200 ,000.
130
00:10:15,290 --> 00:10:22,250
But there is also a 50 % chance of no
delay keeping the total cost at $110
131
00:10:23,050 --> 00:10:29,530
As a result, the EMV here that is the
weighted average will be $155 ,000 for
132
00:10:29,530 --> 00:10:30,670
this subcontractor.
133
00:10:31,410 --> 00:10:36,150
Next, we evaluate the subcontractor B
that is high but reliable one.
134
00:10:36,650 --> 00:10:43,090
With 10 % chance of a 30 -day delay, the
total cost could raise up to $170 ,000.
135
00:10:43,610 --> 00:10:49,930
But in 90 % of the time, there is no
delay and the cost stays at $140 ,000.
136
00:10:50,190 --> 00:10:55,130
So EMV will be $143 ,000 for this
subcontractor.
137
00:10:55,920 --> 00:11:01,540
As a result, although subcontractor B
has a higher bid, its lower risk results
138
00:11:01,540 --> 00:11:04,280
in more favorable expected monetary
value.
139
00:11:04,540 --> 00:11:09,180
Therefore, subcontractor B is a smarter
choice in terms of minimizing expected
140
00:11:09,180 --> 00:11:10,180
cost.
141
00:11:11,020 --> 00:11:15,980
Now let's talk about influence diagrams,
which are powerful tools for analyzing
142
00:11:15,980 --> 00:11:17,740
decisions under uncertainty.
143
00:11:18,180 --> 00:11:22,840
They help us visualize relationships
between three key elements that are
144
00:11:22,840 --> 00:11:28,570
decisions, like whether or not to invest
on certain factors like R &D success
145
00:11:28,570 --> 00:11:32,170
and outcomes such as sales and net
profit.
146
00:11:32,670 --> 00:11:38,410
These diagrams uses arrows to show how
different elements influence each other.
147
00:11:38,950 --> 00:11:43,930
For example, here we see that our R &D
investment decision affects the
148
00:11:43,930 --> 00:11:50,150
likelihood of R &D success, which in
turn influence both sales and net
149
00:11:50,940 --> 00:11:56,280
These models often use probability
distribution to represent uncertainty
150
00:11:56,280 --> 00:11:58,980
evaluated using simulation tools like
Monte Carlo.
151
00:12:03,440 --> 00:12:09,040
FMEA, or failure moods and effects
analysis, is a structured approach that
152
00:12:09,040 --> 00:12:14,160
us identify potential failures in a
process, product, or system, and
153
00:12:14,160 --> 00:12:16,840
how these failures might affect overall
performance.
154
00:12:17,580 --> 00:12:20,520
The key goal of FMEA is to prioritize
risk.
155
00:12:20,760 --> 00:12:25,180
There is done using formula called risk
priority number or RPN.
156
00:12:25,880 --> 00:12:31,660
RPN multiplies three factors that are
severity or how serious the impact of
157
00:12:31,660 --> 00:12:37,460
would be, occurrence or how likely it is
to happen, and detection or how likely
158
00:12:37,460 --> 00:12:39,700
we are to notice the risk before it
happens.
159
00:12:40,510 --> 00:12:45,790
Each of these factors is scored on a
scale from 1 to 10 and by multiplying
160
00:12:45,790 --> 00:12:49,330
scores together we get an RPN between 1
and 1000.
161
00:12:49,930 --> 00:12:54,950
Higher RPN values mean more critical
risks which should be addressed first.
162
00:12:56,570 --> 00:13:01,510
Here we apply the FMEA technique to a
real world ID project that is the
163
00:13:01,510 --> 00:13:03,550
implementation of a new CRM system.
164
00:13:04,150 --> 00:13:09,030
Imagine during the risk identification
phase the team highlighted three major
165
00:13:09,030 --> 00:13:14,480
risks. that are inadequate training for
end users, delays in server delivery by
166
00:13:14,480 --> 00:13:18,200
the supplier, and data transfer errors
from the legacy system.
167
00:13:18,420 --> 00:13:23,640
For each risk, we assessed the severity,
occurrence, and detectability on a
168
00:13:23,640 --> 00:13:27,300
scale from 1 to 10 and then calculated
the risk priority number.
169
00:13:27,700 --> 00:13:33,740
The results show that data transfer
errors had the highest RPN of 315.
170
00:13:34,570 --> 00:13:38,870
This means it is the most critical risk
and should be addressed first in our
171
00:13:38,870 --> 00:13:39,870
mitigation plans.
172
00:13:39,910 --> 00:13:45,850
The other two risks that are inadequate
training with an RPN of 210 and server
173
00:13:45,850 --> 00:13:50,690
delay with an RPN of 96 are still
important but with lower priority.
174
00:13:53,130 --> 00:13:57,410
One of the most important outcomes of
quantitative risk analysis is the risk
175
00:13:57,410 --> 00:14:02,270
report. This report gives us a clear
data -driven picture of the project's
176
00:14:02,270 --> 00:14:06,630
overall exposure to risk using both
numerical results and narrative
177
00:14:07,070 --> 00:14:11,890
It also helps us update the key project
documents including the overall risk
178
00:14:11,890 --> 00:14:17,690
exposure, detailed probabilistic
analysis, and a prioritized list of
179
00:14:17,690 --> 00:14:22,840
risks. In addition, we can track trends
across analysis results and determine
180
00:14:22,840 --> 00:14:25,140
which risk response are most
appropriate.
181
00:14:27,760 --> 00:14:31,800
Before our next session, please make
sure to complete the following readings.
182
00:14:32,100 --> 00:14:38,600
Start with chapters 7 and 9 from
Kendrick and also review sections 11 .3
183
00:14:38,600 --> 00:14:41,320
.4 from the PEMBOX 6th edition.
184
00:14:43,690 --> 00:14:47,130
To reinforce your learning, please
complete the following activities.
185
00:14:47,390 --> 00:14:51,690
First, reply to at least two of your
classmates' posts on Discussion Board 2.
186
00:14:51,890 --> 00:14:57,350
Then take Quiz 3, complete Problem 1,
and submit Project Milestone 3.
187
00:14:58,050 --> 00:15:01,310
Finally, please take a few minutes to
review the Case Study 4.
188
00:15:03,150 --> 00:15:07,550
And that brings us to the end of Part B
of our session on Quantitative Risk
189
00:15:07,550 --> 00:15:11,970
Analysis. If you have any questions,
please don't hesitate to reach out. You
190
00:15:11,970 --> 00:15:12,970
also email me.
191
00:15:13,180 --> 00:15:15,060
Thank you very much for watching this
video
18392
Can't find what you're looking for?
Get subtitles in any language from opensubtitles.com, and translate them here.